
"""simple docstring"""



import argparse
from collections import OrderedDict
from pathlib import Path

import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision.transforms import functional as F

from transformers import DetrImageProcessor, TableTransformerConfig, TableTransformerForObjectDetection
from transformers.utils import logging


logging.set_verbosity_info()
a       :						Dict       		=		logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
a       :						Dict       		=		[]
for i in range(6):
 # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
 rename_keys.append(
     (F"transformer.encoder.layers.{i}.self_attn.out_proj.weight", F"encoder.layers.{i}.self_attn.out_proj.weight")
 )
 rename_keys.append(
     (F"transformer.encoder.layers.{i}.self_attn.out_proj.bias", F"encoder.layers.{i}.self_attn.out_proj.bias")
 )
 rename_keys.append((F"transformer.encoder.layers.{i}.linear1.weight", F"encoder.layers.{i}.fc1.weight"))
 rename_keys.append((F"transformer.encoder.layers.{i}.linear1.bias", F"encoder.layers.{i}.fc1.bias"))
 rename_keys.append((F"transformer.encoder.layers.{i}.linear2.weight", F"encoder.layers.{i}.fc2.weight"))
 rename_keys.append((F"transformer.encoder.layers.{i}.linear2.bias", F"encoder.layers.{i}.fc2.bias"))
 rename_keys.append(
     (F"transformer.encoder.layers.{i}.norm1.weight", F"encoder.layers.{i}.self_attn_layer_norm.weight")
 )
 rename_keys.append((F"transformer.encoder.layers.{i}.norm1.bias", F"encoder.layers.{i}.self_attn_layer_norm.bias"))
 rename_keys.append((F"transformer.encoder.layers.{i}.norm2.weight", F"encoder.layers.{i}.final_layer_norm.weight"))
 rename_keys.append((F"transformer.encoder.layers.{i}.norm2.bias", F"encoder.layers.{i}.final_layer_norm.bias"))
 # decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms
 rename_keys.append(
     (F"transformer.decoder.layers.{i}.self_attn.out_proj.weight", F"decoder.layers.{i}.self_attn.out_proj.weight")
 )
 rename_keys.append(
     (F"transformer.decoder.layers.{i}.self_attn.out_proj.bias", F"decoder.layers.{i}.self_attn.out_proj.bias")
 )
 rename_keys.append(
     (
         F"transformer.decoder.layers.{i}.multihead_attn.out_proj.weight",
         F"decoder.layers.{i}.encoder_attn.out_proj.weight",
     )
 )
 rename_keys.append(
     (
         F"transformer.decoder.layers.{i}.multihead_attn.out_proj.bias",
         F"decoder.layers.{i}.encoder_attn.out_proj.bias",
     )
 )
 rename_keys.append((F"transformer.decoder.layers.{i}.linear1.weight", F"decoder.layers.{i}.fc1.weight"))
 rename_keys.append((F"transformer.decoder.layers.{i}.linear1.bias", F"decoder.layers.{i}.fc1.bias"))
 rename_keys.append((F"transformer.decoder.layers.{i}.linear2.weight", F"decoder.layers.{i}.fc2.weight"))
 rename_keys.append((F"transformer.decoder.layers.{i}.linear2.bias", F"decoder.layers.{i}.fc2.bias"))
 rename_keys.append(
     (F"transformer.decoder.layers.{i}.norm1.weight", F"decoder.layers.{i}.self_attn_layer_norm.weight")
 )
 rename_keys.append((F"transformer.decoder.layers.{i}.norm1.bias", F"decoder.layers.{i}.self_attn_layer_norm.bias"))
 rename_keys.append(
     (F"transformer.decoder.layers.{i}.norm2.weight", F"decoder.layers.{i}.encoder_attn_layer_norm.weight")
 )
 rename_keys.append(
     (F"transformer.decoder.layers.{i}.norm2.bias", F"decoder.layers.{i}.encoder_attn_layer_norm.bias")
 )
 rename_keys.append((F"transformer.decoder.layers.{i}.norm3.weight", F"decoder.layers.{i}.final_layer_norm.weight"))
 rename_keys.append((F"transformer.decoder.layers.{i}.norm3.bias", F"decoder.layers.{i}.final_layer_norm.bias"))

# convolutional projection + query embeddings + layernorm of encoder + layernorm of decoder + class and bounding box heads
rename_keys.extend(
    [
        ('''input_proj.weight''', '''input_projection.weight'''),
        ('''input_proj.bias''', '''input_projection.bias'''),
        ('''query_embed.weight''', '''query_position_embeddings.weight'''),
        ('''transformer.encoder.norm.weight''', '''encoder.layernorm.weight'''),
        ('''transformer.encoder.norm.bias''', '''encoder.layernorm.bias'''),
        ('''transformer.decoder.norm.weight''', '''decoder.layernorm.weight'''),
        ('''transformer.decoder.norm.bias''', '''decoder.layernorm.bias'''),
        ('''class_embed.weight''', '''class_labels_classifier.weight'''),
        ('''class_embed.bias''', '''class_labels_classifier.bias'''),
        ('''bbox_embed.layers.0.weight''', '''bbox_predictor.layers.0.weight'''),
        ('''bbox_embed.layers.0.bias''', '''bbox_predictor.layers.0.bias'''),
        ('''bbox_embed.layers.1.weight''', '''bbox_predictor.layers.1.weight'''),
        ('''bbox_embed.layers.1.bias''', '''bbox_predictor.layers.1.bias'''),
        ('''bbox_embed.layers.2.weight''', '''bbox_predictor.layers.2.weight'''),
        ('''bbox_embed.layers.2.bias''', '''bbox_predictor.layers.2.bias'''),
    ]
)


def 			_UpperCamelCase						(    _A       ,   _A       ,   _A   )     ->	Tuple:





    """simple docstring"""

    _UpperCAmelCase        = state_dict.pop(_A   )
    _UpperCAmelCase        = val


def 			_UpperCamelCase						(    _A   )     ->	str:





    """simple docstring"""

    _UpperCAmelCase        = OrderedDict()
    for key, value in state_dict.items():
        if "backbone.0.body" in key:
            _UpperCAmelCase        = key.replace("""backbone.0.body"""       ,   """backbone.conv_encoder.model"""   )
            _UpperCAmelCase        = value
        else:
            _UpperCAmelCase        = value

    return new_state_dict


def 			_UpperCamelCase						(    _A   )     ->	int:





    """simple docstring"""

    _UpperCAmelCase        = """"""

    # first: transformer encoder
    for i in range(6   ):
        # read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias)
        _UpperCAmelCase        = state_dict.pop(F"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"""   )
        _UpperCAmelCase        = state_dict.pop(F"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_bias"""   )
        # next, add query, keys and values (in that order) to the state dict
        _UpperCAmelCase        = in_proj_weight[:2_5_6, :]
        _UpperCAmelCase        = in_proj_bias[:2_5_6]
        _UpperCAmelCase        = in_proj_weight[2_5_6:5_1_2, :]
        _UpperCAmelCase        = in_proj_bias[2_5_6:5_1_2]
        _UpperCAmelCase        = in_proj_weight[-2_5_6:, :]
        _UpperCAmelCase        = in_proj_bias[-2_5_6:]
    # next: transformer decoder (which is a bit more complex because it also includes cross-attention)
    for i in range(6   ):
        # read in weights + bias of input projection layer of self-attention
        _UpperCAmelCase        = state_dict.pop(F"""{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight"""   )
        _UpperCAmelCase        = state_dict.pop(F"""{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_bias"""   )
        # next, add query, keys and values (in that order) to the state dict
        _UpperCAmelCase        = in_proj_weight[:2_5_6, :]
        _UpperCAmelCase        = in_proj_bias[:2_5_6]
        _UpperCAmelCase        = in_proj_weight[2_5_6:5_1_2, :]
        _UpperCAmelCase        = in_proj_bias[2_5_6:5_1_2]
        _UpperCAmelCase        = in_proj_weight[-2_5_6:, :]
        _UpperCAmelCase        = in_proj_bias[-2_5_6:]
        # read in weights + bias of input projection layer of cross-attention
        _UpperCAmelCase        = state_dict.pop(
            F"""{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight"""   )
        _UpperCAmelCase        = state_dict.pop(F"""{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_bias"""   )
        # next, add query, keys and values (in that order) of cross-attention to the state dict
        _UpperCAmelCase        = in_proj_weight_cross_attn[:2_5_6, :]
        _UpperCAmelCase        = in_proj_bias_cross_attn[:2_5_6]
        _UpperCAmelCase        = in_proj_weight_cross_attn[2_5_6:5_1_2, :]
        _UpperCAmelCase        = in_proj_bias_cross_attn[2_5_6:5_1_2]
        _UpperCAmelCase        = in_proj_weight_cross_attn[-2_5_6:, :]
        _UpperCAmelCase        = in_proj_bias_cross_attn[-2_5_6:]


def 			_UpperCamelCase						(    _A       ,   _A   )     ->	Optional[int]:





    """simple docstring"""

    _UpperCAmelCase     ,_UpperCAmelCase        = image.size
    _UpperCAmelCase        = max(_A       ,   _A   )
    _UpperCAmelCase        = 8_0_0 if """detection""" in checkpoint_url else 1_0_0_0
    _UpperCAmelCase        = target_max_size / current_max_size
    _UpperCAmelCase        = image.resize((int(round(scale * width   )   ), int(round(scale * height   )   ))   )

    return resized_image


def 			_UpperCamelCase						(    _A   )     ->	str:





    """simple docstring"""

    _UpperCAmelCase        = F.to_tensor(_A   )
    _UpperCAmelCase        = F.normalize(_A       ,   mean=[0.485, 0.456, 0.406]       ,   std=[0.229, 0.224, 0.225]   )
    return image


@torch.no_grad()
def 			_UpperCamelCase						(    _A       ,   _A       ,   _A   )     ->	str:





    """simple docstring"""

    logger.info("""Converting model..."""   )

    # load original state dict
    _UpperCAmelCase        = torch.hub.load_state_dict_from_url(_A       ,   map_location="""cpu"""   )
    # rename keys
    for src, dest in rename_keys:
        rename_key(_A       ,   _A       ,   _A   )
    _UpperCAmelCase        = rename_backbone_keys(_A   )
    # query, key and value matrices need special treatment
    read_in_q_k_v(_A   )
    # important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
    _UpperCAmelCase        = """model."""
    for key in state_dict.copy().keys():
        if not key.startswith("""class_labels_classifier"""   ) and not key.startswith("""bbox_predictor"""   ):
            _UpperCAmelCase        = state_dict.pop(_A   )
            _UpperCAmelCase        = val
    # create HuggingFace model and load state dict
    _UpperCAmelCase        = TableTransformerConfig(
        backbone="""resnet18"""       ,   mask_loss_coefficient=1       ,   dice_loss_coefficient=1       ,   ce_loss_coefficient=1       ,   bbox_loss_coefficient=5       ,   giou_loss_coefficient=2       ,   eos_coefficient=0.4       ,   class_cost=1       ,   bbox_cost=5       ,   giou_cost=2       ,   )

    if "detection" in checkpoint_url:
        _UpperCAmelCase        = 1_5
        _UpperCAmelCase        = 2
        _UpperCAmelCase        = {0: """table""", 1: """table rotated"""}
        _UpperCAmelCase        = idalabel
        _UpperCAmelCase        = {v: k for k, v in idalabel.items()}
    else:
        _UpperCAmelCase        = 1_2_5
        _UpperCAmelCase        = 6
        _UpperCAmelCase        = {
            0: """table""",
            1: """table column""",
            2: """table row""",
            3: """table column header""",
            4: """table projected row header""",
            5: """table spanning cell""",
        }
        _UpperCAmelCase        = idalabel
        _UpperCAmelCase        = {v: k for k, v in idalabel.items()}

    _UpperCAmelCase        = DetrImageProcessor(
        format="""coco_detection"""       ,   max_size=8_0_0 if """detection""" in checkpoint_url else 1_0_0_0   )
    _UpperCAmelCase        = TableTransformerForObjectDetection(_A   )
    model.load_state_dict(_A   )
    model.eval()

    # verify our conversion
    _UpperCAmelCase        = """example_pdf.png""" if """detection""" in checkpoint_url else """example_table.png"""
    _UpperCAmelCase        = hf_hub_download(repo_id="""nielsr/example-pdf"""       ,   repo_type="""dataset"""       ,   filename=_A   )
    _UpperCAmelCase        = Image.open(_A   ).convert("""RGB"""   )
    _UpperCAmelCase        = normalize(resize(_A       ,   _A   )   ).unsqueeze(0   )

    _UpperCAmelCase        = model(_A   )

    if "detection" in checkpoint_url:
        _UpperCAmelCase        = (1, 1_5, 3)
        _UpperCAmelCase        = torch.tensor(
            [[-6.7_897, -16.9_985, 6.7_937], [-8.0_186, -22.2_192, 6.9_677], [-7.3_117, -21.0_708, 7.4_055]]   )
        _UpperCAmelCase        = torch.tensor([[0.4_867, 0.1_767, 0.6_732], [0.6_718, 0.4_479, 0.3_830], [0.4_716, 0.1_760, 0.6_364]]   )

    else:
        _UpperCAmelCase        = (1, 1_2_5, 7)
        _UpperCAmelCase        = torch.tensor(
            [[-18.1_430, -8.3_214, 4.8_274], [-18.4_685, -7.1_361, -4.2_667], [-26.3_693, -9.3_429, -4.9_962]]   )
        _UpperCAmelCase        = torch.tensor([[0.4_983, 0.5_595, 0.9_440], [0.4_916, 0.6_315, 0.5_954], [0.6_108, 0.8_637, 0.1_135]]   )

    assert outputs.logits.shape == expected_shape
    assert torch.allclose(outputs.logits[0, :3, :3]       ,   _A       ,   atol=1e-4   )
    assert torch.allclose(outputs.pred_boxes[0, :3, :3]       ,   _A       ,   atol=1e-4   )
    print("""Looks ok!"""   )

    if pytorch_dump_folder_path is not None:
        # Save model and image processor
        logger.info(F"""Saving PyTorch model and image processor to {pytorch_dump_folder_path}..."""   )
        Path(_A   ).mkdir(exist_ok=_A   )
        model.save_pretrained(_A   )
        image_processor.save_pretrained(_A   )

    if push_to_hub:
        # Push model to HF hub
        logger.info("""Pushing model to the hub..."""   )
        _UpperCAmelCase        = (
            """microsoft/table-transformer-detection"""
            if """detection""" in checkpoint_url
            else """microsoft/table-transformer-structure-recognition"""
        )
        model.push_to_hub(_A   )
        image_processor.push_to_hub(_A   )


if __name__ == "__main__":
 a       :						Dict       		=		argparse.ArgumentParser()

 parser.add_argument(
     '''--checkpoint_url''',
     default='''https://pubtables1m.blob.core.windows.net/model/pubtables1m_detection_detr_r18.pth''',
     type=str,
     choices=[
         '''https://pubtables1m.blob.core.windows.net/model/pubtables1m_detection_detr_r18.pth''',
         '''https://pubtables1m.blob.core.windows.net/model/pubtables1m_structure_detr_r18.pth''',
     ],
     help='''URL of the Table Transformer checkpoint you\'d like to convert.''',
 )
 parser.add_argument(
     '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the folder to output PyTorch model.'''
 )
 parser.add_argument(
     '''--push_to_hub''', action='''store_true''', help='''Whether or not to push the converted model to the 🤗 hub.'''
 )
 a       :						int       		=		parser.parse_args()
 convert_table_transformer_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)

"""simple docstring"""



import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    StableDiffusionSAGPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()




class 		a_    (			_UpperCAmelCase  ,    _UpperCAmelCase  ,    unittest.TestCase						):
    a   :					Dict			     =					StableDiffusionSAGPipeline
    a   :					Any			     =					TEXT_TO_IMAGE_PARAMS
    a   :					Optional[Any]			     =					TEXT_TO_IMAGE_BATCH_PARAMS
    a   :					Optional[int]			     =					TEXT_TO_IMAGE_IMAGE_PARAMS
    a   :					Optional[int]			     =					TEXT_TO_IMAGE_IMAGE_PARAMS
    a   :					Tuple			     =					False







    def   _snake_case       (      self      :							List[Any]  )		->Tuple:






        '''simple docstring'''






        torch.manual_seed(0  )
        _UpperCAmelCase        = UNetaDConditionModel(
            block_out_channels=(32, 64)  ,       layers_per_block=2  ,       sample_size=32  ,       in_channels=4  ,       out_channels=4  ,       down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")  ,       up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""")  ,       cross_attention_dim=32  ,       )
        _UpperCAmelCase        = DDIMScheduler(
            beta_start=0.0_0_0_8_5  ,       beta_end=0.0_1_2  ,       beta_schedule="""scaled_linear"""  ,       clip_sample=__UpperCamelCase  ,       set_alpha_to_one=__UpperCamelCase  ,       )
        torch.manual_seed(0  )
        _UpperCAmelCase        = AutoencoderKL(
            block_out_channels=[32, 64]  ,       in_channels=3  ,       out_channels=3  ,       down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""]  ,       up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""]  ,       latent_channels=4  ,       )
        torch.manual_seed(0  )
        _UpperCAmelCase        = CLIPTextConfig(
            bos_token_id=0  ,       eos_token_id=2  ,       hidden_size=32  ,       intermediate_size=37  ,       layer_norm_eps=1e-05  ,       num_attention_heads=4  ,       num_hidden_layers=5  ,       pad_token_id=1  ,       vocab_size=10_00  ,       )
        _UpperCAmelCase        = CLIPTextModel(__UpperCamelCase  )
        _UpperCAmelCase        = CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""  )

        _UpperCAmelCase        = {
            """unet""": unet,
            """scheduler""": scheduler,
            """vae""": vae,
            """text_encoder""": text_encoder,
            """tokenizer""": tokenizer,
            """safety_checker""": None,
            """feature_extractor""": None,
        }
        return components







    def   _snake_case       (      self      :							Union[str, Any]  ,       __UpperCamelCase      :							Union[str, Any]  ,       __UpperCamelCase      :							Any=0  )		->Any:






        '''simple docstring'''






        if str(__UpperCamelCase  ).startswith("""mps"""  ):
            _UpperCAmelCase        = torch.manual_seed(__UpperCamelCase  )
        else:
            _UpperCAmelCase        = torch.Generator(device=__UpperCamelCase  ).manual_seed(__UpperCamelCase  )
        _UpperCAmelCase        = {
            """prompt""": """.""",
            """generator""": generator,
            """num_inference_steps""": 2,
            """guidance_scale""": 1.0,
            """sag_scale""": 1.0,
            """output_type""": """numpy""",
        }
        return inputs







    def   _snake_case       (      self      :							Optional[Any]  )		->Optional[int]:






        '''simple docstring'''






        super().test_inference_batch_single_identical(expected_max_diff=3e-3  )







@slow
@require_torch_gpu
class 		a_    (			unittest.TestCase						):







    def   _snake_case       (      self      :							Union[str, Any]  )		->str:






        '''simple docstring'''






        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()







    def   _snake_case       (      self      :							Tuple  )		->Union[str, Any]:






        '''simple docstring'''






        _UpperCAmelCase        = StableDiffusionSAGPipeline.from_pretrained("""CompVis/stable-diffusion-v1-4"""  )
        _UpperCAmelCase        = sag_pipe.to(__UpperCamelCase  )
        sag_pipe.set_progress_bar_config(disable=__UpperCamelCase  )

        _UpperCAmelCase        = """."""
        _UpperCAmelCase        = torch.manual_seed(0  )
        _UpperCAmelCase        = sag_pipe(
            [prompt]  ,       generator=__UpperCamelCase  ,       guidance_scale=7.5  ,       sag_scale=1.0  ,       num_inference_steps=20  ,       output_type="""np"""  )

        _UpperCAmelCase        = output.images

        _UpperCAmelCase        = image[0, -3:, -3:, -1]

        assert image.shape == (1, 5_12, 5_12, 3)
        _UpperCAmelCase        = np.array([0.1_5_6_8, 0.1_7_3_8, 0.1_6_9_5, 0.1_6_9_3, 0.1_5_0_7, 0.1_7_0_5, 0.1_5_4_7, 0.1_7_5_1, 0.1_9_4_9]  )

        assert np.abs(image_slice.flatten() - expected_slice  ).max() < 5e-2







    def   _snake_case       (      self      :							Union[str, Any]  )		->Tuple:






        '''simple docstring'''






        _UpperCAmelCase        = StableDiffusionSAGPipeline.from_pretrained("""stabilityai/stable-diffusion-2-1-base"""  )
        _UpperCAmelCase        = sag_pipe.to(__UpperCamelCase  )
        sag_pipe.set_progress_bar_config(disable=__UpperCamelCase  )

        _UpperCAmelCase        = """."""
        _UpperCAmelCase        = torch.manual_seed(0  )
        _UpperCAmelCase        = sag_pipe(
            [prompt]  ,       generator=__UpperCamelCase  ,       guidance_scale=7.5  ,       sag_scale=1.0  ,       num_inference_steps=20  ,       output_type="""np"""  )

        _UpperCAmelCase        = output.images

        _UpperCAmelCase        = image[0, -3:, -3:, -1]

        assert image.shape == (1, 5_12, 5_12, 3)
        _UpperCAmelCase        = np.array([0.3_4_5_9, 0.2_8_7_6, 0.2_5_3_7, 0.3_0_0_2, 0.2_6_7_1, 0.2_1_6_0, 0.3_0_2_6, 0.2_2_6_2, 0.2_3_7_1]  )

        assert np.abs(image_slice.flatten() - expected_slice  ).max() < 5e-2







    def   _snake_case       (      self      :							str  )		->str:






        '''simple docstring'''






        _UpperCAmelCase        = StableDiffusionSAGPipeline.from_pretrained("""stabilityai/stable-diffusion-2-1-base"""  )
        _UpperCAmelCase        = sag_pipe.to(__UpperCamelCase  )
        sag_pipe.set_progress_bar_config(disable=__UpperCamelCase  )

        _UpperCAmelCase        = """."""
        _UpperCAmelCase        = torch.manual_seed(0  )
        _UpperCAmelCase        = sag_pipe(
            [prompt]  ,       width=7_68  ,       height=5_12  ,       generator=__UpperCamelCase  ,       guidance_scale=7.5  ,       sag_scale=1.0  ,       num_inference_steps=20  ,       output_type="""np"""  ,       )

        _UpperCAmelCase        = output.images

        assert image.shape == (1, 5_12, 7_68, 3)